Future-House/paper-qa

Features

• RAG Pipelines - Orchestrates an agentic RAG pipeline that iteratively refines queries and gathers evidence across document libraries.
• Question Answering - Provides a retrieval augmented generation system that extracts grounded answers and citations from scientific literature and technical documents.
• Academic Paper Summarizations - Generates contextual summaries of research papers by using re-ranking to identify the most relevant information.
• Agentic Orchestrators - Manages an iterative workflow that refines search queries and executes tools for high-accuracy information synthesis.
• Local Document Indexing - Processes local files into searchable vector stores to enable fast semantic retrieval for RAG workflows.
• Iterative Refinement Workflows - Implements iterative feedback loops between agents to refine search queries and improve the accuracy of retrieved evidence.
• Document Indexing - Builds and stores searchable indexes of document directories to accelerate repeated queries in RAG workflows.
• Grounded Answer Generation - Generates accurate responses supported by traceable in-text citations and source verification from a document library.
• Multimodal Document Processing - Provides a multimodal processing pipeline to extract text, tables, and images from PDFs for LLM consumption.
• Question Answering Systems - Provides a system for retrieving relevant information from scientific literature to generate high-accuracy answers.
• Automated Research Paper Analysis - Extracts grounded answers and summaries from research papers with citations to ensure technical accuracy.
• Retrieval Re-ranking - Employs a two-stage process that first retrieves candidate passages via vectors and then applies a re-ranking model for precision.
• AI-Powered Research Assistants - Functions as an AI research assistant that summarizes academic papers and detects contradictions across documents.
• Multimodal Document Ingestion - Uses vision-based processing to parse complex layouts, tables, and figures from PDFs for better retrieval.
• Multi-Format Document Ingestion - Ingests and normalizes various file types including PDFs, text, markdown, and office documents for AI analysis.
• Local Knowledge Base Indexers - Parses local files and technical documents into a searchable database for semantic retrieval.
• Vector Indexing - Implements a vector-based indexing system for embedding and caching document libraries to enable semantic search.
• Vector Embedding Indexes - Creates searchable representations of text using embedding models to enable efficient semantic retrieval.
• Model Provider Adapters - Provides unified interfaces and adapters to decouple reasoning logic from specific LLM and embedding API providers.
• Cross-Document Contradiction Detection - Identifies conflicting claims across multiple research papers by evaluating specific statements against the literature.
• Local Model Integrations - Integrates with locally hosted language models for text generation and summarization to ensure data privacy.
• Model Provider Configurations - Allows configuration of specific model and embedding providers for reasoning, summarization, and vectorization tasks.
• Prompt Templates - Enables overriding of default system prompts and processing steps to customize output style and logic.
• Embedding Caches - Caches processed document embeddings and parsed text in local files to reduce redundant computation and API costs.
• Bibliographic Metadata Retrievers - Automatically fetches bibliographic data and citation counts from external providers to enrich the document library.
• Metadata Aggregators - Combines bibliographic data and citation licenses from multiple external sources for specific publications.

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